Bangla MEG SaVANT Analysis

Author

Swarnendu Moitra

Code 
path <- getwd()
level_order <- c('Gramm', 'CatViol', 'SemViol')

Load Libraries

Code 
library(lme4)
library(tidyverse)
library(glmm)
library(ggplot2)
library(afex)
library(trimr)
require(car)
require(lattice)
require(itsadug)
library(ggpubr)
library(languageR)
library(plotrix)
library(Hmisc)
library(rlist)
library(effects)
library(sjPlot)
library(effectsize)
library(trimr)
library(jtools)
library(plotrix)
library(ggpubr)
library(gridExtra)
library(grid)
library(ggthemes)
source('data/Themes.R')

Behavioural

Online

MEG

Tark Localiser

6 conditions in this experiment, with 50 trials each. These conditions vary in length and Gaussian noise level as a visual mask. The trials were either 1-element or 4-element items. The 1-element items included 4 unique one-symbol items presented at the lowest Gaussian noise level (level 1), and 26 unique one-letter items presented at both the lowest (level 1) and highest Gaussian noise level (level 24). The one-symbol items were a triangle, a square, a diamond, or a circle each containing a bar-diacritic over the element and the one-letter items consisted single Bangla consonants. The 4-element items were 4-symbol strings presented only at the lowest Gaussian level (level 1) and 50 4-letter monomorphemic Bangla words presented at both noise level 1 and level 24. The four symbols used were again triangle, square, diamond, and circle, and were presented in four different permutations, joined together by a bar. Taking together we had a total of 300 trials in this experiment.

Dummy coded design matrix (2x4)

Noise level coded as 1:0 & 24:1

Stimulus Type coded as letter:0, word:1, symbol:2, string of symbols:3

TYPE I (Working on it)

TYPE II Tark Localiser (130 - 210)ms - Bilateral occipital and temporal regions

Code 
tmin = 130
tmax = 210

# letter clean & lettersymbol :: RH, 150 - 189, p = 0.0164
ls_min = 145
ls_max = 189
p_ls = 0.0164
file_ls = "data/Tark/letter_symbol/TC_letter-nonLetter_bilateral occipital and temporal regions_0.14500000000000002-0.189_rh_p0.0164.csv"

# word clean & word symbols :: RH, 157 - 198 , p = 0.0196
ws_min_rh = 157
ws_max_rh = 198
p_ws_rh = 0.0196

file_ws_rh = "data/Tark/word_symbols/TC_letterWord-nonLetterWord_bilateral occipital and temporal regions_0.157-0.198_rh_p0.0196.csv"

# word clean & word symbols :: LH, 163 - 197 , p = 0.0173
ws_min_lh = 163
ws_max_lh = 197
p_ws_lh = 0.0173
file_ws_lh = "data/Tark/word_symbols/TC_letterWord-nonLetterWord_bilateral occipital and temporal regions_0.163-0.197_lh_p0.0173.csv"

Three Clusters were found!

  1. letter vs. symbol (RH)

  2. word vs. symbols (LH)

  3. word vs. symbols (RH)

1st Cluster : letter vs symbol| RH cluster (145 - 189)ms | p = 0.0164

Code 
data <- read_csv(file_ls, col_names = c("Time", "Participant", "Item.no", "cond", "type","hemi", "dSPM"))
data$Condition <- paste((data$cond),(data$type))
data$Time <- data$Time*1000
#unique(data$Condition)

letterVsymbol <- data %>% filter(Participant != "B0025") %>% filter((cond == "letter" & type =="clean") | (cond == "letter" & type =="symbol") )
letterVsymbol$Condition[letterVsymbol$Condition == "letter clean" ] <- "Letter"
letterVsymbol$Condition[letterVsymbol$Condition == "letter symbol" ] <- "Symbol"  
letterVsymbol <- letterVsymbol %>% 
                  dplyr::group_by(
                    Time,
                    Condition,
                    hemi
                  ) %>%
                  dplyr::summarise(
                    SE = std.error(dSPM),
                    dSPM = mean(dSPM)
                  )

letterVsymbol <- letterVsymbol %>% filter(hemi == "dSPM-lh.stc")
letterVsymbol_Bar <- letterVsymbol %>% filter(Time>= ls_min & Time <= ls_max) %>% dplyr::group_by(Condition) %>% dplyr::summarise(SE = std.error(dSPM), dSPM = mean(dSPM)) 
letterVsymbol_Bar <- letterVsymbol_Bar %>% mutate(Condition = as.factor(Condition))
letterVsymbol_Bar$Condition <- relevel(letterVsymbol_Bar$Condition, ref = "Letter")

RH cluster (145 - 189)ms | p = 0.0164
Code 
letterVsymbol_timeseries <- ggplot(letterVsymbol, aes(x = Time, y = dSPM, col = Condition, linetype = Condition)) + 
  geom_line(aes(group = paste(Condition)),size = 1) +
  
  
  #annotate('rect',xmin=tmin, xmax=tmax, alpha=0.12,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  annotate('text',x=(ls_min + ls_max)/2, y= 0.7, label ="*", size = 20) + 
  annotate('rect',xmin=ls_min, xmax=ls_max, alpha=0.2,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  # annotate('rect',xmin=tmin - 50, xmax=tmax + 50, alpha=0.1,ymin=-Inf,ymax=Inf,fill="green") + 
  # annotate('rect',xmin=-Inf, xmax=tmin, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#000000") + 
  # annotate('rect',xmin=tmax, xmax=Inf, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#666666") + 
  # scale_colour_manual("",values=c("#E63946","#276FBF",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_colour_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_linetype_manual("",values=c("solid", "twodash", "solid", "dashed", "solid", "dashed")) +
  # scale_fill_manual("",values=c("#E63946","#545083",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  geom_ribbon(aes(ymin = dSPM - SE, ymax = dSPM + SE, fill = Condition, group=Condition), alpha = 0.2, col = NA, show.legend = FALSE) + 
  scale_y_continuous("Activation (dSPM)",expand=c(0,0), limits = c(-1, 1)) + scale_x_continuous("Time (ms)", expand=c(0,0), limits = c(-50, 350)) + theme(legend.position="bottom") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12), legend.position = "none") +
  
  geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted")

letterVsymbol_bar <- ggplot(letterVsymbol_Bar, aes(x=Condition, y=dSPM,fill=Condition)) + 
  geom_bar(stat = "identity", width = 0.7, position = position_dodge(0.7), show.legend = FALSE, color="#545083"  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  #theme_Publication()+
  # coord_cartesian(ylim = c(0, 1150)) +
  # scale_y_continuous(expand = expansion(mult = c(0, 0.05)))+
  # geom_signif(
  #   comparisons = list(c("Grammatical", "Pseudowords")),
  #   margin_top = 0.8,
  #   step_increase = 0.09,
  #   tip_length = 0.5,
  #   annotation = c("***")
  # )+
   geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12)) +
  #geom_errorbar function is used to plot error bars
  geom_errorbar(aes(ymin=dSPM-SE,
                     ymax=dSPM+SE,
                 width=0.3))


Plots_bars <- grid.arrange(letterVsymbol_bar + theme_Publication_SwarMoi() ,(letterVsymbol_timeseries + theme_Publication_SwarMoi() +theme(legend.position = "none")),nrow=1, widths=c(4,9));
Figure 1
Code 
knitr::kable(letterVsymbol_Bar)
Condition SE dSPM
Letter 0.0263725 0.1421584
Symbol 0.0251641 -0.2265321

2nd Cluster word vs. symbols: LH cluster (163 - 197)ms | p = 0.0173

Code 
data <- read_csv(file_ws_lh, col_names = c("Time", "Participant", "Item.no", "cond", "type","hemi", "dSPM"))
data$Condition <- paste((data$cond),(data$type))
data$Time <- data$Time*1000
#unique(data$Condition)
wordVsymbols_lh <- data %>% filter(Participant != "B0025") %>%  filter(((cond == "word" & type =="clean") | (cond == "word" & type =="symbols") ))
wordVsymbols_lh$Condition[wordVsymbols_lh$Condition == "word clean" ] <- "Word"
wordVsymbols_lh$Condition[wordVsymbols_lh$Condition == "word symbols" ] <- "Symbols"
wordVsymbols_lh <- wordVsymbols_lh %>% 
                  dplyr::group_by(
                    Time,
                    Condition,
                    hemi
                  ) %>%
                  dplyr::summarise(
                    SE = std.error(dSPM),
                    dSPM = mean(dSPM)
                  )
wordVsymbols_lh <- wordVsymbols_lh %>% filter(hemi == "dSPM-lh.stc")
#wordVsymbols_lh$Condition = with(wordVsymbols_lh, reorder(Word, Symbols)
wordVsymbols_Bar_lh <- wordVsymbols_lh %>% filter(Time>= ws_min_lh & Time <= ws_max_lh) %>% dplyr::group_by(Condition) %>% dplyr::summarise(SE = std.error(dSPM), dSPM = mean(dSPM)) 
wordVsymbols_Bar_lh <- wordVsymbols_Bar_lh %>% mutate(Condition = as.factor(Condition))
wordVsymbols_Bar_lh$Condition <- relevel(wordVsymbols_Bar_lh$Condition, ref = "Word")

LH cluster (163 - 197)ms | p = 0.0173
Code 
wordVsymbols_timeseries_lh <- ggplot(wordVsymbols_lh, aes(x = Time, y = dSPM, col = Condition, linetype = Condition)) + 
  geom_line(aes(group = Condition),size = 1) +
  
  
  #annotate('rect',xmin=tmin, xmax=tmax, alpha=0.12,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  annotate('text',x=(ws_min_lh + ws_max_lh)/2, y= 0.7, label ="*", size = 20) + 
  annotate('rect',xmin=ws_min_lh, xmax=ws_max_lh, alpha=0.2,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  # annotate('rect',xmin=tmin - 50, xmax=tmax + 50, alpha=0.1,ymin=-Inf,ymax=Inf,fill="green") + 
  # annotate('rect',xmin=-Inf, xmax=tmin, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#000000") + 
  # annotate('rect',xmin=tmax, xmax=Inf, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#666666") + 
  # scale_colour_manual("",values=c("#E63946","#276FBF",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_colour_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_linetype_manual("",values=c("solid", "twodash", "solid", "dashed", "solid", "dashed")) +
  # scale_fill_manual("",values=c("#E63946","#545083",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  geom_ribbon(aes(ymin = dSPM - SE, ymax = dSPM + SE, fill = Condition, group=Condition), alpha = 0.2, col = NA, show.legend = FALSE) + 
  scale_y_continuous("Activation (dSPM)",expand=c(0,0), limits = c(-1, 1)) + scale_x_continuous("Time (ms)", expand=c(0,0), limits = c(-50, 350)) + theme(legend.position="bottom") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12), legend.position = "none") +
  
  geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted")

wordVsymbols_bar_lh <- ggplot(wordVsymbols_Bar_lh, aes(x=Condition, y=dSPM,fill=Condition)) + 
  geom_bar(stat = "identity", width = 0.7, position = position_dodge(0.7), show.legend = FALSE, color="#545083"  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  #theme_Publication()+
  # coord_cartesian(ylim = c(0, 1150)) +
  # scale_y_continuous(expand = expansion(mult = c(0, 0.05)))+
  # geom_signif(
  #   comparisons = list(c("Grammatical", "Pseudowords")),
  #   margin_top = 0.8,
  #   step_increase = 0.09,
  #   tip_length = 0.5,
  #   annotation = c("***")
  # )+
   geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12)) +
  #geom_errorbar function is used to plot error bars
  geom_errorbar(aes(ymin=dSPM-SE,
                     ymax=dSPM+SE,
                 width=0.3))
Code 
Plot_wordSymbol_lh <-grid.arrange(wordVsymbols_bar_lh + theme_Publication_SwarMoi() ,(wordVsymbols_timeseries_lh + theme_Publication_SwarMoi() +theme(legend.position = "none")),nrow=1, widths=c(4,9));
Figure 2

3rd Cluster : RH cluster : 157 - 198 ms | p =0.0196

Code 
data <- read_csv(file_ws_rh, col_names = c("Time", "Participant", "Item.no", "cond", "type","hemi", "dSPM"))
data$Condition <- paste((data$cond),(data$type))
data$Time <- data$Time*1000
#unique(data$Condition)

wordVsymbols_rh <- data %>% filter(Participant != "B0025") %>% filter(((cond == "word" & type =="clean") | (cond == "word" & type =="symbols") ))
wordVsymbols_rh$Condition[wordVsymbols_rh$Condition == "word clean" ] <- "Word"
wordVsymbols_rh$Condition[wordVsymbols_rh$Condition == "word symbols" ] <- "Symbols"  
wordVsymbols_rh <- wordVsymbols_rh %>% 
                  dplyr::group_by(
                    Time,
                    Condition,
                    hemi
                  ) %>%
                  dplyr::summarise(
                    SE = std.error(dSPM),
                    dSPM = mean(dSPM)
                  )
wordVsymbols_rh <- wordVsymbols_rh %>% filter(hemi == "dSPM-rh.stc")
#wordVsymbols_lh$Condition = with(wordVsymbols_lh, reorder(Word, Symbols)
wordVsymbols_Bar_rh <- wordVsymbols_rh %>% filter(Time>= ws_min_rh & Time <= ws_max_rh) %>% dplyr::group_by(Condition) %>% dplyr::summarise(SE = std.error(dSPM), dSPM = mean(dSPM)) 
wordVsymbols_Bar_rh <- wordVsymbols_Bar_rh %>% mutate(Condition = as.factor(Condition))
wordVsymbols_Bar_rh$Condition <- relevel(wordVsymbols_Bar_rh$Condition, ref = "Word")
Code 
knitr::kable(wordVsymbols_Bar_rh)
Condition SE dSPM
Symbols 0.0418006 -0.1435638
Word 0.0289144 0.1655991
Code 
wordVsymbols_timeseries_rh <- ggplot(wordVsymbols_rh, aes(x = Time, y = dSPM, col = Condition, linetype = Condition)) + 
  geom_line(aes(group = Condition),size = 1) +
  
  
  #annotate('rect',xmin=tmin, xmax=tmax, alpha=0.12,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  annotate('text',x=(ws_min_rh + ws_max_rh)/2, y= 0.7, label ="*", size = 20) + 
  annotate('rect',xmin=ws_min_rh, xmax=ws_max_rh, alpha=0.2,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  # annotate('rect',xmin=tmin - 50, xmax=tmax + 50, alpha=0.1,ymin=-Inf,ymax=Inf,fill="green") + 
  # annotate('rect',xmin=-Inf, xmax=tmin, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#000000") + 
  # annotate('rect',xmin=tmax, xmax=Inf, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#666666") + 
  # scale_colour_manual("",values=c("#E63946","#276FBF",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_colour_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_linetype_manual("",values=c("solid", "twodash", "solid", "dashed", "solid", "dashed")) +
  # scale_fill_manual("",values=c("#E63946","#545083",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  geom_ribbon(aes(ymin = dSPM - SE, ymax = dSPM + SE, fill = Condition, group=Condition), alpha = 0.2, col = NA, show.legend = FALSE) + 
  scale_y_continuous("Activation (dSPM)",expand=c(0,0), limits = c(-1, 1)) + scale_x_continuous("Time (ms)", expand=c(0,0), limits = c(-50, 350)) + theme(legend.position="bottom") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12), legend.position = "none") +
  
  geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted")

wordVsymbols_bar_rh <- ggplot(wordVsymbols_Bar_rh, aes(x=Condition, y=dSPM,fill=Condition)) + 
  geom_bar(stat = "identity", width = 0.7, position = position_dodge(0.7), show.legend = FALSE, color="#545083"  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  #theme_Publication()+
  # coord_cartesian(ylim = c(0, 1150)) +
  # scale_y_continuous(expand = expansion(mult = c(0, 0.05)))+
  # geom_signif(
  #   comparisons = list(c("Grammatical", "Pseudowords")),
  #   margin_top = 0.8,
  #   step_increase = 0.09,
  #   tip_length = 0.5,
  #   annotation = c("***")
  # )+
   geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12)) +
  #geom_errorbar function is used to plot error bars
  geom_errorbar(aes(ymin=dSPM-SE,
                     ymax=dSPM+SE,
                 width=0.3))
#wordVsymbols_timeseries_rh
#wordVsymbols_bar_rh

RH cluster : 157 - 198 ms | p =0.0196
Code 
Plot_wordSymbol_rh <-grid.arrange(wordVsymbols_bar_rh + theme_Publication_SwarMoi() ,(wordVsymbols_timeseries_lh + theme_Publication_SwarMoi() +theme(legend.position = "none")),nrow=1, widths=c(4,9))
Figure 3

Decomposition

aROI - LMM

Cluster 1

Code 
col_names = c("Participant", "Item.no", "Prefix", "hemi", "wordFreq", "stemFreq", "ITEMNO", "Condition", "stim","dSPM")
aROI_Cluster_1_Tark <- read_csv("data/M170/Tark_Cluster_1/aROI_Cluster_1_Tark.csv", col_names = col_names)

GA_aROI_Cluster_1_Tark <- aROI_Cluster_1_Tark %>% filter(Condition != "SemViol" & Condition != "CatViol") %>%  group_by(Condition) %>% dplyr::summarise(GA_dSPM = mean(dSPM))

knitr::kable(GA_aROI_Cluster_1_Tark)
Condition GA_dSPM
Filler 0.0315876
Gramm 0.0284522

Cluster 2

Code 
# col_names = c("Participant", "Item.no", "Prefix", "hemi", "wordFreq", "stemFreq", "ITEMNO", "Condition", "stim","dSPM")
# aROI_Cluster_2_Tark <- read_csv("data/M170/Tark_Cluster_1/aROI_Cluster_2_Tark.csv", col_names = col_names)
# 
# GA_aROI_Cluster_2_Tark <- aROI_Cluster_1_Tark %>% filter(Condition != "SemViol" & Condition != "CatViol") %>%  group_by(Condition) %>% dplyr::summarise(GA_dSPM = mean(dSPM))

Cluster 3

FROI

Code 
col_names = c("Participant", "Item.no", "Prefix", "hemi", "wordFreq", "stemFreq", "ITEMNO", "Condition", "stim","dSPM")

M170_Tark_fROI <- read_csv("data/M170/froi_TP_M170_Tark.csv", col_names = col_names)
M170_Tark_fROI <- M170_Tark_fROI %>% 
  filter(hemi == "dSPM-lh.stc") %>% 
  filter(Condition != "SemViol" & Condition != "CatViol") %>%
  select(-stim) %>% 
  mutate (log_stemFreq = log(stemFreq), log_wordFreq =log(wordFreq), TP = (wordFreq/stemFreq), logTP = log(TP), logTP_div = (log_wordFreq/log_stemFreq))

M170_Tark_fROI <- M170_Tark_fROI %>% 
  mutate(
    Participant = as.factor(Participant),
    dSPM = as.numeric(dSPM),
    Condition = as.factor(Condition),
    item = as.factor(ITEMNO)
  )

#str(M170_Tark_fROI)

M170_Tark_fROI$Prefix <- relevel(M170_Tark_fROI$Condition, ref="Gramm")

M170_Tark_fROI_main <- lmer(dSPM ~ Condition * logTP_div + log_stemFreq + log_wordFreq + (1|Participant) + (1|ITEMNO), data = M170_Tark_fROI, REML = F)
tab_model(M170_Tark_fROI_main)
  dSPM
Predictors Estimates CI p
(Intercept) -0.33 -1.26 – 0.60 0.483
Condition [Gramm] -0.25 -0.55 – 0.05 0.101
logTP div -0.00 -0.01 – -0.00 0.037
log stemFreq 0.13 -0.06 – 0.33 0.185
log wordFreq 0.06 -0.10 – 0.21 0.471
Condition [Gramm] × logTP
div		 0.10 -0.28 – 0.47 0.608
Random Effects
σ2 11.90
τ00 ITEMNO 0.03
τ00 Participant 0.08
ICC 0.01
N Participant 24
N ITEMNO 152
Observations 3622
Marginal R2 / Conditional R2 0.003 / 0.012
Code 
summary(M170_Tark_fROI_main)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
  method [lmerModLmerTest]
Formula: dSPM ~ Condition * logTP_div + log_stemFreq + log_wordFreq +  
    (1 | Participant) + (1 | ITEMNO)
   Data: M170_Tark_fROI

     AIC      BIC   logLik deviance df.resid 
 19293.0  19348.8  -9637.5  19275.0     3613 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-12.4629  -0.1537  -0.0225   0.1211  17.5714 

Random effects:
 Groups      Name        Variance Std.Dev.
 ITEMNO      (Intercept)  0.03431 0.1852  
 Participant (Intercept)  0.07602 0.2757  
 Residual                11.89978 3.4496  
Number of obs: 3622, groups:  ITEMNO, 152; Participant, 24

Fixed effects:
                           Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)               -0.333311   0.475113 154.573657  -0.702   0.4840  
ConditionGramm            -0.251188   0.153226 149.732006  -1.639   0.1032  
logTP_div                 -0.004343   0.002084 159.671443  -2.084   0.0388 *
log_stemFreq               0.134060   0.101076 151.242770   1.326   0.1867  
log_wordFreq               0.056309   0.078029 150.936565   0.722   0.4716  
ConditionGramm:logTP_div   0.097669   0.190187 148.627268   0.514   0.6083  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) CndtnG lgTP_d lg_stF lg_wrF
ConditnGrmm -0.098                            
logTP_div   -0.098 -0.008                     
log_stemFrq -0.968 -0.078  0.097              
log_wordFrq -0.955  0.153  0.053  0.928       
CndtnGr:TP_  0.304 -0.270  0.016 -0.236 -0.404
Code 
library(interactions)

interact_plot(M170_Tark_fROI_main, pred = "logTP_div", modx = "Condition", interval = TRUE, int.type = "confidence", int.width = .8, x.label = "Transition Probability", y.label = "dSPM", color.class = "Oranges")

Recomposition

Code 
X <- tribble(
  ~Prefix, ~Condition, ~PrefixType,
  "CatViolDU", "CatViol", "DU", 
  "CatViolPROTI", "CatViol","PROTI", 
  "GrammDU", "Gramm","DU",
  "GrammPROTI", "Gramm","PROTI",
  "SemViolDU", "SemViol","DU",
  "SemViolPROTI", "SemViol","PROTI",
  "Filler", "Filler","Fill",
)

TL: RH_TemporalLobe_244-286_p0.0197

Code 
RH_Temporal <- read_csv("data/SAVANT/CatViol_RH_244-286_p0.0197.csv", col_names = c("Participant", "Prefix","hemi", "dSPM"))
RH_Temporal = RH_Temporal %>% left_join(X)
RH_Temporal <- RH_Temporal %>% select(!hemi)  %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),dSPM = mean(dSPM))
RH_Temporal_TC <- read_csv("data/SAVANT/TC_CatViol_RH_244-286_p0.0197.csv", col_names = c("time", "Participant", "Prefix","hemi", "dSPM"))
RH_Temporal_TC = RH_Temporal_TC %>% left_join(X)

RH_Temporal_TC$time = RH_Temporal_TC$time*1000

RH_Temporal_TC <- RH_Temporal_TC %>% filter(between(time, 244, 286)) %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))

RH_temporal_plot <- RH_Temporal %>% filter(Condition != "Filler")  %>% ggplot(aes(x= factor(Condition, level = level_order),y=dSPM,fill=Condition)) +
  # geom_bar function is used to plot bars of barplot
  geom_bar(stat = "identity", width = 0.95, position = position_dodge(0.1), show.legend = FALSE  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual(values = c("blue" , "green" ,"red"))+
   geom_errorbar(aes(ymin=dSPM-SE,
                    ymax=dSPM+SE,
                 width=0.3))+ 
  theme_minimal() +
  theme(axis.text.x = element_text(size = 15, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(size = 18, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(size = 18, angle = 90, hjust = .5, vjust = .5, face = "plain"))

RH_temporal_plot

Code 
knitr::kable(RH_Temporal)
Condition SE dSPM
CatViol 0.0711853 0.3463524
Filler 0.0794799 -0.0110849
Gramm 0.0786636 -0.0136021
SemViol 0.0952327 -0.1208228

OF : LH OF 424-452_p0.0472

Code 
LH_OF <- read_csv("data/SAVANT/OF_LH_424-452_p0.0472.csv", col_names = c("Participant", "Prefix","hemi", "dSPM"))
LH_OF = LH_OF %>% left_join(X)
LH_OF <- LH_OF %>% select(!hemi)  %>% group_by(Condition,Prefix, PrefixType) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))
LH_OF_TC <- read_csv("data/SAVANT/TC_OF_LH_424-452_p0.0472.csv", col_names = c("time", "Participant", "Prefix","hemi", "dSPM"))
LH_OF_TC = LH_OF_TC %>% left_join(X)
LH_OF_TC$time = LH_OF_TC$time*1000
LH_OF_TC <- LH_OF_TC %>% filter(between(time, 424, 452)) %>% group_by(Condition, Prefix, PrefixType) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))


LH_OF_plot <- LH_OF %>% filter(Condition != "Filler")  %>% ggplot(aes(x= factor(Condition, level = level_order),y=dSPM,fill=Condition)) +
  # geom_bar function is used to plot bars of barplot
  geom_bar(stat = "identity", width = 0.99, position = position_dodge(0.1), show.legend = FALSE  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  facet_wrap(~PrefixType) +
  # scale_fill_manual(values = c("blue", "lightblue" , "green", "lightgreen" ,"red", "pink"))+
   scale_fill_manual(values = c("blue",  "green","red"))+
   geom_errorbar(aes(ymin=dSPM-SE,
                    ymax=dSPM+SE,
                 width=0.3))+ 
  theme_minimal() +
  theme(axis.text.x = element_text(size = 18, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(size = 18, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(size = 18, angle = 90, hjust = .5, vjust = .5, face = "plain"))

LH_OF_plot

Code 
knitr::kable(LH_OF)
Condition Prefix PrefixType SE dSPM
CatViol CatViolDU DU 0.0713381 -0.1174651
CatViol CatViolPROTI PROTI 0.2385963 0.4050528
Filler Filler Fill 0.0655281 0.1002821
Gramm GrammDU DU 0.1590589 0.2822480
Gramm GrammPROTI PROTI 0.0938074 -0.1899715
SemViol SemViolDU DU 0.1309587 0.2741620
SemViol SemViolPROTI PROTI 0.1841838 -0.4534022

Exploratory Analysis

OF : LH_OF Early (238-279_p0.0049)

Code 
LH_OF_early  <- read_csv("data/SAVANT/CatViol_LH_earlyOF_238-279_p0.0049.csv", col_names = c("Participant", "Prefix","hemi", "dSPM"))
LH_OF_early = LH_OF_early %>% left_join(X)

LH_OF_early <- LH_OF_early %>% select(!hemi)  %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))
LH_OF_early_TC <- read_csv("data/SAVANT/TC_CatViol_LH_earlyOF_238-279_p0.0049.csv", col_names = c("time", "Participant", "Prefix","hemi", "dSPM"))
LH_OF_early_TC = LH_OF_early_TC %>% left_join(X)
LH_OF_early_TC$time = LH_OF_early_TC$time*1000
LH_OF_early_TC <- LH_OF_early_TC %>% filter(between(time, 238, 279)) %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))

LH_OF_early_plot <- LH_OF_early %>% filter(Condition != "Filler")  %>% ggplot(aes(x= factor(Condition, level = level_order),y=dSPM,fill=Condition)) +
  # geom_bar function is used to plot bars of barplot
  geom_bar(stat = "identity", width = 0.95, position = position_dodge(0.1), show.legend = FALSE  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual(values = c("blue" , "green" ,"red"))+
   geom_errorbar(aes(ymin=dSPM-SE,
                    ymax=dSPM+SE,
                 width=0.3))+ 
  theme_minimal() +
  theme(axis.text.x = element_text(size = 15, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(size = 18, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(size = 18, angle = 90, hjust = .5, vjust = .5, face = "plain"))

LH_OF_early_plot

Code 
knitr::kable(LH_OF_early)
Condition SE dSPM
CatViol 0.0411778 -0.1254958
Filler 0.0313287 -0.0434605
Gramm 0.0910601 0.1492997
SemViol 0.0756533 0.0737754